Photo-realistic face synthesis and reenactment with deep generative models

The advent of Deep Learning has led to numerous breakthroughs in the field of Computer Vision. Over the last decade, a significant amount of research has been undertaken towards designing neural networks for visual data analysis. At the same time, rapid advancements have been made towards the direction of deep generative modeling, especially after the introduction of Generative Adversarial Networks (GANs), which have shown particularly promising results when it comes to synthesising visual data. Since then, considerable attention has been devoted to the problem of photo-realistic human face animation due to its wide range of applications, including image and video editing, virtual assistance, social media, teleconferencing, and augmented reality. The objective of this thesis is to make progress towards generating photo-realistic videos of human faces. To that end, we propose novel generative algorithms that provide explicit control over the facial expression and head pose of synthesised subjects. Despite the major advances in face reenactment and motion transfer, current methods struggle to generate video portraits that are indistinguishable from real data. In this work, we aim to overcome the limitations of existing approaches, by combining concepts from deep generative networks and video-to-video translation with 3D face modelling, and more specifically by capitalising on prior knowledge of faces that is enclosed within statistical models such as 3D Morphable Models (3DMMs). In the first part of this thesis, we introduce a person-specific system that performs full head reenactment using ideas from video-to-video translation. Subsequently, we propose a novel approach to controllable video portrait synthesis, inspired from Implicit Neural Representations (INR). In the second part of the thesis, we focus on person-agnostic methods and present a GAN-based framework that performs video portrait reconstruction, full head reenactment, expression editing, novel pose synthesis and face frontalisation.Open Acces

Group invariance principles for causal generative models

The postulate of independence of cause and mechanism (ICM) has recently led to several new causal discovery algorithms. The interpretation of independence and the way it is utilized, however, varies across these methods. Our aim in this paper is to propose a group theoretic framework for ICM to unify and generalize these approaches. In our setting, the cause-mechanism relationship is assessed by comparing it against a null hypothesis through the application of random generic group transformations. We show that the group theoretic view provides a very general tool to study the structure of data generating mechanisms with direct applications to machine learning.Comment: 16 pages, 6 figure

On the Design, Implementation and Application of Novel Multi-disciplinary Techniques for explaining Artificial Intelligence Models

284 p.Artificial Intelligence is a non-stopping field of research that has experienced some incredible growth lastdecades. Some of the reasons for this apparently exponential growth are the improvements incomputational power, sensing capabilities and data storage which results in a huge increment on dataavailability. However, this growth has been mostly led by a performance-based mindset that has pushedmodels towards a black-box nature. The performance prowess of these methods along with the risingdemand for their implementation has triggered the birth of a new research field. Explainable ArtificialIntelligence. As any new field, XAI falls short in cohesiveness. Added the consequences of dealing withconcepts that are not from natural sciences (explanations) the tumultuous scene is palpable. This thesiscontributes to the field from two different perspectives. A theoretical one and a practical one. The formeris based on a profound literature review that resulted in two main contributions: 1) the proposition of anew definition for Explainable Artificial Intelligence and 2) the creation of a new taxonomy for the field.The latter is composed of two XAI frameworks that accommodate in some of the raging gaps found field,namely: 1) XAI framework for Echo State Networks and 2) XAI framework for the generation ofcounterfactual. The first accounts for the gap concerning Randomized neural networks since they havenever been considered within the field of XAI. Unfortunately, choosing the right parameters to initializethese reservoirs falls a bit on the side of luck and past experience of the scientist and less on that of soundreasoning. The current approach for assessing whether a reservoir is suited for a particular task is toobserve if it yields accurate results, either by handcrafting the values of the reservoir parameters or byautomating their configuration via an external optimizer. All in all, this poses tough questions to addresswhen developing an ESN for a certain application, since knowing whether the created structure is optimalfor the problem at hand is not possible without actually training it. However, some of the main concernsfor not pursuing their application is related to the mistrust generated by their black-box" nature. Thesecond presents a new paradigm to treat counterfactual generation. Among the alternatives to reach auniversal understanding of model explanations, counterfactual examples is arguably the one that bestconforms to human understanding principles when faced with unknown phenomena. Indeed, discerningwhat would happen should the initial conditions differ in a plausible fashion is a mechanism oftenadopted by human when attempting at understanding any unknown. The search for counterfactualsproposed in this thesis is governed by three different objectives. Opposed to the classical approach inwhich counterfactuals are just generated following a minimum distance approach of some type, thisframework allows for an in-depth analysis of a target model by means of counterfactuals responding to:Adversarial Power, Plausibility and Change Intensity

Stability and Expressiveness of Deep Generative Models

In den letzten Jahren hat Deep Learning sowohl das maschinelle Lernen als auch die maschinelle Bildverarbeitung revolutioniert. Viele klassische Computer Vision-Aufgaben, wie z.B. die Objekterkennung und semantische Segmentierung, die traditionell sehr anspruchsvoll waren, können nun mit Hilfe von überwachten Deep Learning-Techniken gelöst werden. Überwachtes Lernen ist ein mächtiges Werkzeug, wenn annotierte Daten verfügbar sind und die betrachtete Aufgabe eine eindeutige Lösung hat. Diese Bedingungen sind allerdings nicht immer erfüllt. Ein vielversprechender Ansatz ist in diesem Fall die generative Modellierung. Im Gegensatz zu rein diskriminativen Modellen können generative Modelle mit Unsicherheiten umgehen und leistungsfähige Modelle lernen, auch wenn keine annotierten Trainingsdaten verfügbar sind. Obwohl aktuelle Ansätze zur generativen Modellierung vielversprechende Ergebnisse erzielen, beeinträchtigen zwei Aspekte ihre Expressivität: (i) Einige der erfolgreichsten Ansätze zur Modellierung von Bilddaten werden nicht mehr mit Hilfe von Optimierungsalgorithmen trainiert, sondern mit Algorithmen, deren Dynamik bisher nicht gut verstanden wurde. (ii) Generative Modelle sind oft durch den Speicherbedarf der Ausgaberepräsentation begrenzt. In dieser Arbeit gehen wir auf beide Probleme ein: Im ersten Teil der Arbeit stellen wir eine Theorie vor, die es erlaubt, die Trainingsdynamik von Generative Adversarial Networks (GANs), einem der vielversprechendsten Ansätze zur generativen Modellierung, besser zu verstehen. Wir nähern uns dieser Problemstellung, indem wir minimale Beispielprobleme des GAN-Trainings vorstellen, die analytisch verstanden werden können. Anschließend erhöhen wir schrittweise die Komplexität dieser Beispiele. Dadurch gewinnen wir neue Einblicke in die Trainingsdynamik von GANs und leiten neue Regularisierer her, die auch für allgemeine GANs sehr gut funktionieren. Insbesondere ermöglichen unsere neuen Regularisierer erstmals, ein GAN mit einer Auflösung von einem Megapixel zu trainieren, ohne dass wir die Auflösung der Trainingsverteilung schrittweise erhöhen müssen. Im zweiten Teil dieser Arbeit betrachten wir Ausgaberepräsentationen für generative Modelle in 3D und für 3D-Rekonstruktionstechniken. Durch die Einführung von impliziten Repräsentationen sind wir in der Lage, viele Techniken, die in 2D funktionieren, auf den 3D-Bereich auszudehnen ohne ihre Expressivität einzuschränken.In recent years, deep learning has revolutionized both machine learning and computer vision. Many classical computer vision tasks (e.g. object detection and semantic segmentation), which traditionally were very challenging, can now be solved using supervised deep learning techniques. While supervised learning is a powerful tool when labeled data is available and the task under consideration has a well-defined output, these conditions are not always satisfied. One promising approach in this case is given by generative modeling. In contrast to purely discriminative models, generative models can deal with uncertainty and learn powerful models even when labeled training data is not available. However, while current approaches to generative modeling achieve promising results, they suffer from two aspects that limit their expressiveness: (i) some of the most successful approaches to modeling image data are no longer trained using optimization algorithms, but instead employ algorithms whose dynamics are not well understood and (ii) generative models are often limited by the memory requirements of the output representation. We address both problems in this thesis: in the first part we introduce a theory which enables us to better understand the training dynamics of Generative Adversarial Networks (GANs), one of the most promising approaches to generative modeling. We tackle this problem by introducing minimal example problems of GAN training which can be understood analytically. Subsequently, we gradually increase the complexity of these examples. By doing so, we gain new insights into the training dynamics of GANs and derive new regularizers that also work well for general GANs. Our new regularizers enable us - for the first time - to train a GAN at one megapixel resolution without having to gradually increase the resolution of the training distribution. In the second part of this thesis we consider output representations in 3D for generative models and 3D reconstruction techniques. By introducing implicit representations to deep learning, we are able to extend many techniques that work in 2D to the 3D domain without sacrificing their expressiveness

Machine learning for particle identification in the LHCb detector

LHCb experiment is a specialised b-physics experiment at the Large Hadron Collider at CERN. It has a broad physics program with the primary objective being the search for CP violations that would explain the matter-antimatter asymmetry of the Universe. LHCb studies very rare phenomena, making it necessary to process millions of collision events per second to gather enough data in a reasonable time frame. Thus software and data analysis tools are essential for the success of the experiment. Particle identification (PID) is a crucial ingredient of most of the LHCb results. The quality of the particle identification depends a lot on the data processing algorithms. This dissertation aims to leverage the recent advances in machine learning field to improve the PID at LHCb. The thesis contribution consists of four essential parts related to LHCb internal projects. Muon identification aims to quickly separate muons from the other charged particles using only information from the Muon subsystem. The second contribution is a method that takes into account a priori information on label noise and improves the accuracy of a machine learning model for classification of this data. Such data are common in high-energy physics and, in particular, is used to develop the data-driven muon identification methods. Global PID combines information from different subdetectors into a single set of PID variables. Cherenkov detector fast simulation aims to improve the speed of the PID variables simulation in Monte-Carlo